Machine Learning Analysis of Supernova Light Curves

Авторы: Pruzhinskaya M., Malanchev K.L., Kornilov M., de Oliveira Ishida E.E., Mondon F., Volnova A.A., Korolev V.
Сборник: Proceedings of Science
Серия: Accretion Processes in Cosmic Sources – II (APCS2018) - Accretion onto White Dwarfs, Neutron Stars & Black Holes
Том: 342
Год издания: 2020
Место издания: Sissa Medialab Trieste, Italy
Первая страница: 1
DOI: 10.22323/1.342.0051
Аннотация: The next generation of astronomical surveys will revolutionize our understanding of the Universe, raising unprecedented data challenges in the process. One of them is the impossibility to rely on human scanning for the identification of unusual/unpredicted astrophysical objects. Moreover, given that most of the available data will be in the form of photometric observations, such characterization cannot rely on the existence of high resolution spectroscopic observations. We introduce an analysis of anomaly detection in the Open Supernova Catalog (http://sne.space/) with use of machine learning. We developed a strategy and pipeline — where anomalous objects are identified and then submitted to careful individual analysis. This project represents an effective strategy to guarantee we shall not overlook exciting new science hidden in the data we fought so hard to acquire.
Добавил в систему: Пружинская Мария Викторовна

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